Online platforms have become a focus nowadays as an instrument to help expedite some daily routines such as e-commerce including food ordering, online banking, social networking, and many more. Food ordering through online platforms, for instance, have evolved due to the growing numbers of users of digital platforms, restaurant-goers, and many more. The recent pandemic of coronavirus has changed the scenario of brick-and-mortar businesses while most governments forced to shut down and impose strict regulations of lockdown and social distancing among citizens. Although this situation hit most businesses, statistics have shown there is an increasing amount of E-Commerce spending globally. There has been several commercial systems and studies in the market and literature related to food ordering platforms and processes discussed in the study. However, most systems focusing on order-to-delivery and imposed a higher price for delivery. This study introduces an O2O food ordering concept aiming the takeout customers segment and targeting to optimised orders that can be made in advanced and on-the-go. The concept generates models of a new O2O and distance food ordering for potential E-Commerce implementations. Out of the models, process improvement is analysed and suggest that customers would save time, effort, and able to maintain social distancing among citizens. The models can be generalised to be implemented in various industries and situations, depending on in-depth analysis that would be carried out in future studies.
Abstract. Injection moulding is the most widely used processes in manufacturing plastic products. Since the quality of injection improves plastic parts are mostly influenced by process conditions, the method to determine the optimum process conditions becomes the key to improving the part quality. This paper presents a systematic methodology to analyse the shrinkage of the thick plate part during the injection moulding process. Genetic Algorithm (GA) method was proposed to optimise the process parameters that would result in optimal solutions of optimisation goals. Using the GA, the shrinkage of the thick plate part was improved by 39.1% in parallel direction and 17.21% in the normal direction of melt flow.
Abstract. In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA). The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA) method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX) model. The performance of the model is validated using mean squares error (MSE) between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square). Based on the results obtained, MSE produce from Bat Algorithm (BA) is outperformed the Least Square (LS) method.
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